SoroushVahidi/scidocs huggingface dataset:
Shape matching and object recognition using low distortion correspondences
(2005)

We approach recognition in the framework of deformable shape matching, relying on a new algorithm for finding correspondences between feature points. This algorithm sets up correspondence as an integer quadratic programming problem, where the cost function has terms based on similarity of corresponding geometric blur point descriptors as well as the geometric distortion between pairs of corresponding feature points. The algorithm handles outliers, and thus enables matching of exemplars to query images in the presence of occlusion and clutter. Given the correspondences, we estimate an aligning transform, typically a regularized thin plate spline, resulting in a dense correspondence between the two shapes. Object recognition is then handled in a nearest neighbor framework where the distance between exemplar and query is the matching cost between corresponding points. We show results on two datasets. One is the Caltech 101 dataset (Fei-Fei, Fergus and Perona), an extremely challenging dataset with large intraclass variation. Our approach yields a 48% correct classification rate, compared to Fei-Fei et al 's 16%. We also show results for localizing frontal and profile faces that are comparable to special purpose approaches tuned to faces.
SUN database: Large-scale scene recognition from abbey to zoo
(2010)

Scene categorization is a fundamental problem in computer vision. However, scene understanding research has been constrained by the limited scope of currently-used databases which do not capture the full variety of scene categories. Whereas standard databases for object categorization contain hundreds of different classes of objects, the largest available dataset of scene categories contains only 15 classes. In this paper we propose the extensive Scene UNderstanding (SUN) database that contains 899 categories and 130,519 images. We use 397 well-sampled categories to evaluate numerous state-of-the-art algorithms for scene recognition and establish new bounds of performance. We measure human scene classification performance on the SUN database and compare this with computational methods. Additionally, we study a finer-grained scene representation to detect scenes embedded inside of larger scenes.
Shape matching and object recognition using shape contexts
(2002)

This paper presents my work on computing shape models that are computationally fast and invariant basic transformations like translation, scaling and rotation. In this paper, I propose shape detection using a feature called shape context. Shape context describes all boundary points of a shape with respect to any single boundary point. Thus it is descriptive of the shape of the object. Object recognition can be achieved by matching this feature with a priori knowledge of the shape context of the boundary points of the object. Experimental results are promising on handwritten digits, trademark images.
Backward Stochastic Differential Equations and Applications
(2010)

A new type of stochastic differential equation, called the backward stochastic differentil equation (BSDE), where the value of the solution is prescribed at the final (rather than the initial) point of the time interval, but the solution is nevertheless required to be at each time a function of the past of the underlying Brownian motion, has been introduced recently, independently by Peng and the author in [16], and by Dufne and Epstein in [7]. This class of equations is a natural nonlinear extension of linear equations that appear both as the equation for the adjoint process in the maximum principle for optimal stochastic control (see [2]), and as a basic model for asset pricing in financial mathematics. It was soon after discovered (see [22], [17]) that those BSDEs provide probabilistic formulas for solutions of certain semilinear partial differential equations (PDEs), which generalize the well-known Feynmann-Kac formula for second order linear PDEs. This provides a new additional tool for analyzing solutions of certain PDEs, for instance reaction-diffusion equations.
Face detection using SURF cascade
(2011)

We present a novel boosting cascade based face detection framework using SURF features. The framework is derived from the well-known Viola-Jones (VJ) framework but distinguished by two key contributions. First, the proposed framework deals with only several hundreds of multidimensional local SURF patches instead of hundreds of thousands of single dimensional haar features in the VJ framework. Second, it takes AUC as a single criterion for the convergence test of each cascade stage rather than the two conflicting criteria (false-positive-rate and detection-rate) in the VJ framework. These modifications yield much faster training convergence and much fewer stages in the final cascade. We made experiments on training face detector from large scale database. Results shows that the proposed method is able to train face detectors within one hour through scanning billions of negative samples on current personal computers. Furthermore, the built detector is comparable to the state-of-the-art algorithm not only on the accuracy but also on the processing speed.
Automatic detection and classification of diabetic retinopathy stages using CNN
(2017)

A Convolutional Neural Networks (CNNs) approach is proposed to automate the method of Diabetic Retinopathy(DR) screening using color fundus retinal photography as input. Our network uses CNN along with denoising to identify features like micro-aneurysms and haemorrhages on the retina. Our models were developed leveraging Theano, an open source numerical computation library for Python. We trained this network using a high-end GPU on the publicly available Kaggle dataset. On the data set of over 30,000 images our proposed model achieves around 95% accuracy for the two class classification and around 85% accuracy for the five class classification on around 3,000 validation images.
Qualitative research in health care. Assessing quality in qualitative research.
(2000)

In the past decade, qualitative methods have become more commonplace in areas such as health services research and health technology assessment, and there has been a corresponding rise in the reporting of qualitative research studies in medical and related journals. Interest in these methods and their wider exposure in health research has led to necessary scrutiny of qualitative research. Researchers from other traditions are increasingly concerned to understand qualitative methods and, most importantly, to examine the claims researchers make about the findings obtained from these methods. The status of all forms of research depends on the quality of the methods used. In qualitative research, concern about assessing quality has manifested itself recently in the proliferation of guidelines for doing and judging qualitative work. Users and funders of research have had an important role in developing these guidelines as they become increasingly familiar with qualitative methods, but require some means of assessing their quality and of distinguishing “good” and “poor” quality research. However, the issue of “quality” in qualitative research is part of a much larger and contested debate about the nature of the knowledge produced by qualitative research, whether its quality can legitimately be judged, and, if so, how. This paper cannot do full justice to this wider epistemological debate. Rather it outlines two views of how qualitative methods might be judged and argues that qualitative research can be assessed according to two broad criteria: validity and relevance.
An empirical examination of factors contributing to the creation of successful e-learning environments
(2008)

Although existing models of e-learning effectiveness in information systems (IS) have increased our understanding of how technology can support and enhance learning, most of our models do not take into account the importance of social presence. Thus, this study extends previous research by developing a model of e-learning effectiveness which adds social presence to other oft studied variables including application-specific computer self-efficacy (AS-CSE), perceived usefulness, course interaction, and e-learning effectiveness. Using data from 345 individuals, this model was validated through a field study in an introductory IS survey course. Results indicate that AS-CSE and perceived usefulness were related to course performance, course satisfaction, and course instrumentality. In addition, course interaction was related to course performance and satisfaction. Finally, social presence was related to course satisfaction and course instrumentality. Implications for research and practice are discussed. r 2007 Elsevier Ltd. All rights reserved.
peepHole : Securing your homes with Raspberry Pi and Amazon Echo
(2016)

In recent years, the field of Internet of Things (IoT) has seen significant investments made by the research community and the industry. Specifically, the Smart Home space has been a prime focus with the introduction of devices such as Amazon Echo, Google Home, Samsung Smart Things among others. The growth of an industry results in innovative, economic, and advanced solutions. In this paper, we focus on smart home security and how to build a robust, cost-effective system that can be widely used. We power our system using Amazon Echo, Amazon’s cloud services, its speech services. A Raspberry Pi with a camera module is used as the hardware component for providing home security. We describe the different components of our product and we show that our system works effectively to identify the person at the doorstep, and have thus named our system peepHole. Keywords—Intenet of Things, Smart Homes, Home Security, Amazon Web Services, Amazon Echo, Raspberry Pi, Face Verification
Chemical structure recognition and prediction: A machine learning technique
(2018)

A chemical structure recognition system designed to identify and predict hand-drawn chemical diagrams is presented. Although the proposed system is primarily intended to help chemists complete their chemical structure drawings and predict and complete the chemical structure, it is also envisioned to be utilized as a learning aid in chemistry education on a student's touchscreen device or on a teacher's smart board. The proposed system continuously provides the user with real-time feedback about the most relevant combinations to complete the chemical compound. A constantly updated list of prioritized candidate structures is displayed to the user. If the user is an expert, he or she will be allowed to contribute to the learning process of the proposed system. The proposed system deploys two trainable Bayesian networks, one at its input and another one at its output. Between the two networks, the classification of hand- drawn chemical structure is done using morphological image processing, LDA and PCA, incorporating therefore both image- based and feature-based techniques. This incorporation contributes considerably to the increased performance of the proposed system by enabling it to efficiently recognize messy drawings and noisy sketches like those containing over-traced strokes. Performance comparison with existing state of the art techniques proved the system to be outperforming in terms of recognition accuracy.
Breath acetone monitoring by portable Si:WO3 gas sensors.
(2012)

Breath analysis has the potential for early stage detection and monitoring of illnesses to drastically reduce the corresponding medical diagnostic costs and improve the quality of life of patients suffering from chronic illnesses. In particular, the detection of acetone in the human breath is promising for non-invasive diagnosis and painless monitoring of diabetes (no finger pricking). Here, a portable acetone sensor consisting of flame-deposited and in situ annealed, Si-doped epsilon-WO(3) nanostructured films was developed. The chamber volume was miniaturized while reaction-limited and transport-limited gas flow rates were identified and sensing temperatures were optimized resulting in a low detection limit of acetone (∼20ppb) with short response (10-15s) and recovery times (35-70s). Furthermore, the sensor signal (response) was robust against variations of the exhaled breath flow rate facilitating application of these sensors at realistic relative humidities (80-90%) as in the human breath. The acetone content in the breath of test persons was monitored continuously and compared to that of state-of-the-art proton transfer reaction mass spectrometry (PTR-MS). Such portable devices can accurately track breath acetone concentration to become an alternative to more elaborate breath analysis techniques.
Performance analysis of latent fingerprint enhancement techniques
(2017)

Fingerprint Enhancement is an important step used to improve the accuracy and rate of automated detection of fingerprints. Enhancing the latent fingerprints facilitates the extraction of minutiae from the fingerprint image. In this paper, two methods of latent fingerprint enhancement are carried out. The first uses Histogram Equalization for enhancing the latent fingerprint. The second method uses a novel approach of Binarization followed by Wiener filtering and an additional Median filter. The improvements in the image are then analyzed based on their entropy, contrast and moment.
Text Generation From Tables
(2019)

This paper proposes a neural generative model, namely Table2Seq, to generate a natural language sentence based on a table. Specifically, the model maps a table to continuous vectors and then generates a natural language sentence by leveraging the semantics of a table. Since rare words, e.g., entities and values, usually appear in a table, we develop a flexible copying mechanism that selectively replicates contents from the table to the output sequence. We conduct extensive experiments to demonstrate the effectiveness of our Table2Seq model and the utility of the designed copying mechanism. On the WIKIBIO and SIMPLEQUESTIONS datasets, the Table2Seq model improves the state-of-the-art results from 34.70 to 40.26 and from 33.32 to 39.12 in terms of BLEU-4 scores, respectively. Moreover, we construct an open-domain dataset WIKITABLETEXT that includes 13 318 descriptive sentences for 4962 tables. Our Table2Seq model achieves a BLEU-4 score of 38.23 on WIKITABLETEXT outperforming template-based and language model based approaches. Furthermore, through experiments on 1 M table-query pairs from a search engine, our Table2Seq model considering the structured part of a table, i.e., table attributes and table cells, as additional information outperforms a sequence-to-sequence model considering only the sequential part of a table, i.e., table caption.
Standards for smart education – towards a development framework
(2018)

Smart learning environments (SLEs) utilize a range of digital technologies in supporting learning, education and training; they also provide a prominent signpost for how future learning environments might be shaped. Thus, while innovation proceeds, SLEs are receiving growing attention from the research community, outputs from which are discussed in this paper. Likewise, this broad application of educational digital technologies is also the remit of standardization in an ISO committee, also discussed in this paper. These two communities share a common interest in, conceptualizing this emerging domain with the aim to identifying direction to further development. In doing so, terminology issues arise along with key questions such as, ‘how is smart learning different from traditional learning?’ Presenting a bigger challenge is the question, ‘how can standardization work be best scoped in today's innovation-rich, networked, cloud-based and data-driven learning environments?’ In responding, this conceptual paper seeks to identify candidate constructs and approaches that might lead to stable, coherent and exhaustive understanding of smart learning environments, thereby providing standards development for learning, education and training a needed direction. Based on reviews of pioneering work within smart learning, smart education and smart learning environments we highlight two models, a cognitive smart learning model and a smartness level model. These models are evaluated against current standardization challenges in the field of learning, education and training to form the basis for a development platform for new standards in this area.
Early Detection of Sudden Pedestrian Crossing for Safe Driving During Summer Nights
(2017)

Sudden pedestrian crossing (SPC) is the major reason for pedestrian-vehicle crashes. In this paper, we focus on detecting SPCs at night for supporting an advanced driver assistance system using a far-infrared (FIR) camera mounted on the front roof of a vehicle. Although the thermal temperature of the road is similar to or higher than that of the pedestrians during summer nights, many previous researches have focused on pedestrian detection during the winter, spring, or autumn seasons. However, our research concentrates on SPC during the hot summer season because the number of collisions between pedestrians and vehicles in Korea is higher at that time than during the other seasons. For real-time processing, we first decide the optimal levels of image scaling and search area. We then use our proposed method for detecting virtual reference lines that are associated with road segmentation without using color information and change these lines according to the turning direction of the vehicle. Pedestrian detection is conducted using a cascade random forest with low-dimensional Haar-like features and oriented center-symmetric local binary patterns. The SPC is predicted based on the likelihood and the spatiotemporal features of the pedestrians, such as their overlapping ratio with virtual reference lines, as well as the direction and magnitude of each pedestrian’s movement. The proposed algorithm was successfully applied to various pedestrian data sets captured by an FIR camera, and the results show that its SPC detection performance is better than those of other methods.
Experiences with rapid mobile game development using unity engine
(2011)

In this work, we describe our experiences with FunCopter, a casual game we have designed and implemented using Unity engine, suitable for portable devices. We emphasize some general principles, particularly with respect to rapid game development and constrained graphics. In addition to that, we present the main activities realized in each software engineering phase and the lessons we learned during its development. These descriptions include details from the initial concept to the fully realized, playable game. This work will share general but important experiences with Unity, particularly interesting for game developers in beginner's level.
Dimensionality reduction for the quantitative evaluation of a smartphone-based Timed Up and Go test
(2011)

The Timed Up and Go is a clinical test to assess mobility in the elderly and in Parkinson's disease. Lately instrumented versions of the test are being considered, where inertial sensors assess motion. To improve the pervasiveness, ease of use, and cost, we consider a smartphone's accelerometer as the measurement system. Several parameters (usually highly correlated) can be computed from the signals recorded during the test. To avoid redundancy and obtain the features that are most sensitive to the locomotor performance, a dimensionality reduction was performed through principal component analysis (PCA). Forty-nine healthy subjects of different ages were tested. PCA was performed to extract new features (principal components) which are not redundant combinations of the original parameters and account for most of the data variability. They can be useful for exploratory analysis and outlier detection. Then, a reduced set of the original parameters was selected through correlation analysis with the principal components. This set could be recommended for studies based on healthy adults. The proposed procedure could be used as a first-level feature selection in classification studies (i.e. healthy-Parkinson's disease, fallers-non fallers) and could allow, in the future, a complete system for movement analysis to be incorporated in a smartphone.
Let's stop trying to be "sexy" - preparing managers for the (big) data-driven business era
(2017)

Let's stop trying to be 'sexy' – preparing managers for the (big) data-driven business era Kevin Daniel André Carillo, Article information: To cite this document: Kevin Daniel André Carillo, (2017) "Let's stop trying to be 'sexy' – preparing managers for the (big) data-driven business era", Business Process Management Journal, Vol. 23 Issue: 3, doi: 10.1108/BPMJ-09-2016-0188 Permanent link to this document: http://dx.doi.org/10.1108/BPMJ-09-2016-0188
The neurobiology of social cognition
(2001)

Recent studies have begun to elucidate the roles played in social cognition by specific neural structures, genes, and neurotransmitter systems. Cortical regions in the temporal lobe participate in perceiving socially relevant stimuli, whereas the amygdala, right somatosensory cortices, orbitofrontal cortices, and cingulate cortices all participate in linking perception of such stimuli to motivation, emotion, and cognition. Open questions remain about the domain-specificity of social cognition, about its overlap with emotion and with communication, and about the methods best suited for its investigation.
Conformal Surface Parameterization for Texture Mapping
(2000)

In this paper , we give an explicit methodfor mappingany simply connectedsurfaceonto the spherein a mannerwhich preservesangles. This techniquerelieson certainconformalmappingsfrom differentialgeometry. Our methodprovidesa new way to automaticallyassigntexturecoordinates to complex undulatingsurfaces. We demonstratea finite elementmethod thatcanbeusedto applyour mappingtechniqueto a triangulatedgeometric descriptionof a surface.
The ARTEMIS European driving cycles for measuring car pollutant emissions.
(2004)

In the past 10 years, various work has been undertaken to collect data on the actual driving of European cars and to derive representative real-world driving cycles. A compilation and synthesis of this work is provided in this paper. In the frame of the European research project: ARTEMIS, this work has been considered to derive a set of reference driving cycles. The main objectives were as follows: to derive a common set of reference real-world driving cycles to be used in the frame of the ARTEMIS project but also in the frame of on-going national campaigns of pollutant emission measurements, to ensure the compatibility and integration of all the resulting emission data in the European systems of emission inventory; to ensure and validate the representativity of the database and driving cycles by comparing and taking into account all the available data regarding driving conditions; to include in three real-world driving cycles (urban, rural road and motorway) the diversity of the observed driving conditions, within sub-cycles allowing a disaggregation of the emissions according to more specific driving conditions (congested and free-flow urban). Such driving cycles present a real advantage as they are derived from a large database, using a methodology that was widely discussed and approved. In the main, these ARTEMIS driving cycles were designed using the available data, and the method of analysis was based to some extent on previous work. Specific steps were implemented. The study includes characterisation of driving conditions and vehicle uses. Starting conditions and gearbox use are also taken into account.